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Google Generative-AI-Leader Exam Syllabus Topics:

SectionWeightObjectives
Fundamentals of generative AI30%- Describe how various data types are used in gen AI and the business implications.
  • 1. Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format)
  • 2. Identifying the differences between labeled and unlabeled data
  • 3. Identifying the differences between structured and unstructured data, and identifying real world examples of each type
- Identify the core layers of the gen AI landscape and the business implications.
  • 1. Infrastructure
  • 2. Models
  • 3. Platforms
  • 4. Applications
  • 5. Agents
- Describe core generative AI (gen AI) concepts and use cases.
  • 1. Identifying the stages of the machine learning lifecycle (e.g., data ingestion, data preparation, model training, model deployment, model management) and the Google Cloud tools for each stage
  • 2. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement)
  • 3. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost)
  • 4. Defining core gen AI concepts (e.g., artificial intelligence, natural language processing, machine learning, generative AI, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, large language models)
Google Cloud's generative AI offerings35%- Identify the use cases and strengths of Google's foundation models.
  • 1. Gemma
  • 2. Imagen
  • 3. Veo
  • 4. Gemini
- Describe Google Cloud's gen AI product and service portfolio.
  • 1. Gemini for Google Cloud
  • 2. Vertex AI
  • 3. Vertex AI Studio
  • 4. Model Garden
  • 5. Google Workspace
Business strategies for a successful gen AI solution15%- Describe best practices for a successful gen AI project.
  • 1. Choosing the right model
  • 2. Building a business case
  • 3. Evaluating AI solutions
- Describe Google's approach to responsible AI and its importance.
  • 1. Google's AI principles
  • 2. Responsible AI best practices
- Describe change management best practices and their importance.
  • 1. Creating a culture of innovation
  • 2. Enabling AI adoption
Techniques to improve gen AI model output20%- Describe how grounding can be used to improve model output.
  • 1. Grounding with Google Search
  • 2. Grounding with enterprise data
- Describe prompt engineering techniques and their purpose.
  • 1. Few-shot
  • 2. Chain of thought
  • 3. Zero-shot
  • 4. One-shot
- Describe the process of fine-tuning gen AI models.
  • 1. Supervised tuning
  • 2. Reinforcement learning from human feedback (RLHF)

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q104-Q109):

NEW QUESTION # 104
A company is developing an AI character for a video game. The AI character needs to learn how to navigate a complex environment and make decisions to achieve certain objectives within the game. When the AI takes actions that lead to positive outcomes, like finding a reward or overcoming an obstacle, it receives a positive score. When it takes actions that lead to negative outcomes, like hitting a wall or losing progress, it receives a negative score. Through this process of trial and error, the AI gradually improves the character's ability to play the game effectively. What machine learning should the company use?

Answer: D

Explanation:
This scenario perfectly describes reinforcement learning. In reinforcement learning, an agent learns to make decisions by interacting with an environment, receiving1 rewards for desirable actions and penalties for undesirable ones,2 and iteratively improving its behavior through trial and error to maximize cumulative reward.
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NEW QUESTION # 105
A pharmaceutical company's research and development department spends significant time manually reviewing new scientific papers to identify potential drug targets. They need a solution that can answer questions about these documents and provide summarized insights to researchers without requiring extensive coding expertise. What should the organization do?

Answer: A

Explanation:
The requirement is to answer questions about the documents and provide summarized insights without requiring extensive coding expertise. Vertex AI Agent Builder is designed precisely for creating custom AI agents, often with low-code or no-code capabilities, that can interact with and process large volumes of information like scientific papers. While Vertex AI Search could index papers for keyword searches, it doesn't directly answer questions or provide summarized insights in the same way a generative AI agent built with Agent Builder could. Gemini for Google Workspace is for collaborative work, not specifically for building custom AI agents for document analysis. Vertex AI AutoML is for training classification models, which is different from answering questions and summarizing.
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NEW QUESTION # 106
What does a diffusion model do?

Answer: D

Explanation:
A Diffusion Model (or Denoising Diffusion Probabilistic Model) is a specific class of generative AI model that is best known for its ability to create highly realistic images (e.g., Google's Imagen and Stable Diffusion are based on this architecture).
The core mechanism of a diffusion model is a two-step process:
Forward Diffusion (Adding Noise): It learns how to gradually corrupt data (like an image) by adding random noise until the original content is completely indistinguishable.
Reverse Diffusion (Denoising): It then learns to reverse this process-to gradually remove the noise-starting from a random noise pattern and iteratively refining it, guided by a text prompt, until a clear, coherent, and high-quality piece of content (an image or video) is generated.
Option D accurately captures this mechanism: the model starts with pure noise and generates the final structured data (the image) by refining that noise.
Option A describes predictive AI (forecasting models).
Option C describes a database or storage service.
Option B describes a workflow agent or optimization AI.
(Reference: Google's training materials on Foundation Models define Diffusion Models as generative models that operate by gradually converting a state of random noise into a structured, meaningful output, most commonly for the generation of high-quality images and video.)


NEW QUESTION # 107
An organization needs an AI tool to analyze and summarize lengthy customer feedback text transcripts. You need to choose a Google foundation model with a large context window. What foundation model should the organization choose?

Answer: B

Explanation:
Gemini models are known for their large context windows, making them highly suitable for processing and summarizing lengthy texts like customer feedback transcripts. CodeGemma is specialized for code, Imagen for image generation, and Chirp for speech.
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NEW QUESTION # 108
The office of the CISO wants to use generative AI (gen AI) to help automate tasks like summarizing case information, researching threats, and taking actions like creating detection rules. What agent should they use?

Answer: D

Explanation:
Given the tasks involve researching threats and creating detection rules, the most appropriate and specialized agent would be a Security agent. This type of agent would be pre-configured or easily adaptable to understand security-specific contexts, data, and actions within a CISO's domain.


NEW QUESTION # 109
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